Although we focused on the processing methods to reduce the amount of training data, other perspectives may also be considered to achieve the same goal.
For instance, a physics-informed neural network (PINN) [1]} has recently been attracting attention since it can take a constraint based on physical laws as a loss function.
Thanks to this characteristic, it is highly expected that models with the concept of PINN can be trained accordingly from a small amount of training data while satisfying the physical laws.
Otherwise, the use of other sophisticated neural networks, e.g., graph neural network [2]} and reservoir computing [3]}, may be one of the possible candidates to improve the interpretability and generalizability, although careful choice is required depending on users' problem settings.
Moreover, from the perspective of data reconstruction from limited sensors, an optimal sensor placement derived with the theoretical manner can also drive a practical utilization of neural networks [4]}, [5]}, [6]}.
We hope that our proposed techniques of internal procedure visualization and data bulking are able to encourage the practical use of neural networks in the fluid dynamics community, by combining with these aforementioned tools.
